Abstract

Osteoporosis and disorders of bone fragility are highly heritable, but despite much
effort the identities of few of the genes involved has been established. Recent developments
in genetics such as genome-wide association studies are revolutionizing research in
this field, and it is likely that further contributions will be made through application
of next-generation sequencing technologies, analysis of copy number variation polymorphisms,
and high-throughput mouse mutagenesis programs. This article outlines what we know
about osteoporosis genetics to date and the probable future directions of research
in this field.

Introduction

Ninety years ago a major debate took place between the Mendelians and the Biometricians.
Mendel's laws of inheritance (with their clear phenotype-genotype correlation) were
inadequate to explain heritable and normally distributed quantitative traits such
as height, bone mineral density (BMD), and weight. The elegant solution to this problem
was that both parties were right; single genes cannot underlie inheritance of complex
quantitative traits, but such traits arise due to the action of multiple genes, each
inherited in Mendelian fashion and each exerting their individual effect upon the
ultimate phenotype. Over the past century many monogenic diseases with classical Mendelian
inheritance have successfully been mapped, but progress in dissection of quantitative
trait loci has – until very recently – been frankly disappointing. Now, quantum leaps
in genotyping and bioinformatics capacity have at last provided an opportunity to
unravel the genetic basis of quantitative traits that underlie human disease.

Osteoporosis represents a paradigm in this area: a common and disabling disease in
which the phenotype is caused by the effects of multiple quantitative trait loci.
Approaches to identify genes in which rare mutations have a large phenotypic effect
had been extremely successful in mapping monogenic bone diseases (for example, osteoporosis-pseudoglioma),
and certainly such genetic studies identified hitherto unexpected pathways that also
contribute to osteoporosis. However, until very recently, there had been little return
from extensive efforts to identify common genetic polymorphisms in the multiple genes,
each of small individual effect, that ultimately result in the phenotype of osteoporosis.
It is therefore illuminating to review the genetics of osteoporosis not only in its
specifics but also as a model for the dissection of other complex genetic disorders
– from genetic epidemiology, candidate gene association studies, and linkage studies
to whole-genome association studies – and to consider future directions.

The problem

Osteoporosis is a common condition of elderly men and women, which manifests clinically
by minimal trauma fractures, particularly vertebral and hip fracture. Almost a quarter
of European women aged over 50 years are osteoporotic according to World Health Organization
criteria for BMD (t-score < -2.5), and the remaining lifetime risk for any osteoporotic
hip and vertebral fracture in 50-year-old Caucasian women is 39% [1]. Osteoporosis is not confined to women, as is evident particularly in older age groups,
in which up to 40% of hip fractures occur in men [2].

The economic and social costs of osteoporosis represent a huge drain of health resources.
In 2005 there were approximately 2 million osteoporotic fractures in the USA, with
health care costs estimated at US$17 billion [3]. This cost was expected to rise by 50% by 2025. In Sweden osteoporotic fracture is
responsible for more hospital bed-days than breast cancer and prostate cancer combined
[4]. Osteoporosis is not just a problem for the developed world. Rapid population growth
and aging populations in both developed and developing countries mean that worldwide
osteoporotic fracture rates are expected to increase. The brunt of these costs will
be faced by developing countries that are least equipped to cope.

Currently, most therapeutic options retard the rate of bone loss but they do not convert
osteoporosis back to normal bone mass. Only a few anabolic agents exist, although
generally such options are too costly to be practicable, even for wealthy countries.
Current screening methods to identify at-risk individuals have only moderate predictive
capacity and as such are not suitable for general population use. The only hope to
reverse the oncoming worldwide hip fracture tsunami will be if radical changes are
made in our understanding, prevention, and treatment of osteoporosis. Genetics research
offers the potential to elucidate the disease process more fully, to identify new
targets for therapeutic intervention, and to refine prognostic tests in order to improve
targeting of primary prevention measures to those most in need.

Genetic epidemiology

The first step in any condition thought to have an underlying genetic aetiology is
to establish whether a trait (such as low BMD or fracture) really is heritable. From
there, modeling can predict the likely mode of inheritance, demonstrate the appropriate
method for investigation (for instance, family versus general population, selected
versus nonselected population), and inform power calculations to ensure that an appropriate
study of adequate size is performed.

Twin and family studies have demonstrated that osteoporosis is highly familial, and
that the tendency of the condition to run in families is predominantly due to genetic
factors. This is true of a wide range of osteoporosis-related phenotypes, including
BMD, bone turnover, and skeletal dimensions associated with growth and fracture risk
[5-8], as well as fracture risk itself [9].

There has been extensive debate and research within the bone research community about
the optimal phenotype to study. The ultimate goal of research in osteoporosis genetics
is to identify genes that increase bone fragility. It would therefore seem enticing
to study fracture as the primary outcome variable. However, fractures can occur for
a wide variety of reasons, some of which are unrelated to bone fragility, and it is
likely to prove genetically more complex than intermediate bone phenotypes, such as
BMD.

BMD (as measured using dual energy x-ray absorptiometry) is the screening tool most
commonly used to identify patients with osteoporosis and who are at increased risk
for low-trauma fracture. The heritability of BMD, measured using a variety of methods
in twin and intergenerational studies, has been shown to be very high. Studies of
female twins have shown heritability of BMD to be 57% to 92% [10-12], including studies of postmenopausal twins [13]. Estimates from intergenerational family studies have also identified substantial
heritability of BMD (44% to 67%) [14-16]. Several segregation studies, in families drawn from the general population, and
ascertained with probands with more severe phenotypes, have demonstrated that the
majority of the heritability of BMD is polygenic [14,16-20]. In specific populations substantial monogenic effects have been observed, but this
has always been on the background of predominantly polygenic effects [19,21-23].

Therefore most genetic studies in osteoporosis to date have focused on the phenotype
of BMD, because it is highly heritable, easy to measure, and has an established strong
relationship with fracture risk. However, areal BMD (bone quantity per unit bone area
measured) does not provide information regarding bone distribution (between cortical
and cancellous compartments) or bone microarchitecture. Large and small bones with
different volumetric BMD (bone quantity per unit bone volume measured) and fracture
risk may have similar areal BMD. Methods to determine bone architectural measures
and bone fragility indices from areal BMD scans make inappropriate assumptions about
similarity of bone shape between individuals, and therefore have not proven better
predictors of fracture risk than areal BMD itself.

There has been significant interest in noninvasive assessment of bone microarchitecture.
Data from murine studies in particular indicate that although a large proportion of
genetic variants can be identified by studies of BMD alone, significant additional
information can be obtained with use of more informative bone imaging modalities,
such as quantitative computed tomography scanning and magnetic resonance imaging.
These methods are still in development, however, and will not be suitable for large-scale
genetic studies until there is better standardization of measures, and until their
genetic epidemiology and clinical significance are better established in humans.

Fracture risk is known to run in families, with the relative risk ratio of a fracture
in a first-degree relative ranging from 1.3 to 2.4, varying according to the type
of relative pair and site of fracture [24,25]. Fracture heritability studies in twins and families have generally found more limited
heritability than for BMD, with the possible single exception of hip fracture in younger
cohorts (age < 69 years). In a national Finnish cohort of 15,098 twins [26], no significant increase in monozygotic twin concordance for fracture was observed.
The findings of this study have been debated, and a reanalysis suggested that the
data were consistent with a 35% heritability of fracture liability (significance level
not reported) [27]. A study of 6,750 British twins [28] found significant heritability of 54% for Colles' fracture in women. Michaelson and
coworkers [29] studied 33,432 Swedish twins and reported age-adjusted heritability of any fracture
of 16%, osteoporotic fractures of 27%, and hip fracture of 46%. A significant age
interaction was observed, with the heritability of fracture being highest when the
fracture occurred at a younger age (heritability of 68% at age < 69 years), and no
heritability observed at older ages, when most hip fractures occur (heritability 3%
at age > 79 years). Deng and colleagues [25] demonstrated low heritability of Colles' fracture in a study of 6,274 sisters or
mothers of women who had had a previous Colles' fracture (heritability 25.4%; significance
level not reported). In a separate study of 50 Caucasian families [9], they demonstrated no significant heritability of wrist and spinal fractures, and
heritability of hip fracture was only of marginal significance (P = 0.048, uncorrected for the multiple, albeit correlated, phenotypes studied). That
study, and the British twin study referred to above, suggested that the genetic correlation
between hip fracture and BMD was low. This appears to contradict several seminal reports
on the genetic epidemiology of osteoporosis, demonstrating that premenopausal daughters
of mothers with osteoporotic fracture have low BMD [30-32].

Overall, the fracture studies suggest that fracture has a lower heritability than
BMD, particularly among the elderly. Thus, although it is clearly important to determine
whether BMD-associated polymorphisms influence bone fragility (the ultimate question),
the most powerful approach is likely to be initial screens targeting genes that affect
BMD, with subsequent testing to determine the relevance of such genes to fracture.
The obvious disadvantage of this approach is that if genes influence bone fragility
and fracture risk independent of BMD, then this approach will not identify them. However,
the evidence from BMD and fracture associated genes to date is that nearly all BMD-associated
genes are also fracture associated.

What genes are known to cause osteoporosis?

Until the development of genome-wide association studies, researchers employed family-based
linkage techniques and conducted candidate gene association studies in their valiant
attempts to identify osteoporosis genes. Monogenic skeletal diseases affecting BMD
are summarized in Table 1; genetic associations with general community BMD are summarized elsewhere [33]. As was the general experience with these approaches in other complex genetic diseases,
the signal-to-noise ratio was not sufficient to permit robust identification of any
particular genes involved, with one notable exception – the gene encoding lipoprotein-related
receptor protein 5 (LRP5).

The role played by this gene in bone was first identified from rare monogenic diseases,
using classical linkage approaches followed by fine mapping and candidate gene screening.
Inactivating mutations cause the autosomal-recessive condition osteoporosis-pseudoglioma,
with low BMD observed in obligate carriers [34]. Activating mutations result in the autosomal-dominant conditions of high bone mass
syndrome [35,36]. Subsequent studies rapidly demonstrated that the gene played a significant role
in the general population [37,38], a finding also confirmed in Asian populations [39-41]. Association was also observed with fracture risk [42,43]. The association of LRP5 with bone density is apparent even in childhood, indicating a likely effect on bone
accrual [37,44]. Carriers of LRP5 variants have BMD 0.17 to 0.57 standard deviations away from the population mean [45,46].

As discussed below, two studies [46,47] recently demonstrated association of LRP5 with BMD, achieving genome-wide significance (P < 10-7). The importance of these studies is not just in confirming the significance of LRP5, which was already established: rather, they serve as proof-of-concept that whole-genome-wide
association approaches successfully identify quantitative trait loci that underlie
BMD and osteoporosis.

These genetic findings have stimulated major research programs into the LRP5/Wnt signaling
pathway as a major pathway in skeletal development and as a potential therapeutic
target for osteoporosis. Of particular interest are treatments targeting sclerostin
(encoded by the gene SOST), which is thought to inhibit LRP5. Mutations in SOST cause a high bone mass syndrome and van Buchem disease, which is a form of osteopetrosis
with low fracture risk [48,49]. Common polymorphisms of SOST have also been demonstrated to be associated with general population variation in
BMD [45,50], although this has been less well studied than LRP5. Anti-sclerostin antibodies are currently in clinical trials and are showing promise
as anabolic agents in osteoporosis. Thus, new therapeutic modalities are already in
place as a direct consequence of genetic research in osteoporosis.

A large number of other candidate genes have been implicated in one study or another
as being associated with osteoporosis. Many of these are likely to be true positive
findings, but in our opinion few have sufficiently robust evidence to be considered
'established', without needing further confirmation. As such, their significance is
currently hard to judge. Similarly, although several areas have been linked with BMD
in family studies, in no case has the evidence of linkage been sufficiently strong
as to be considered robust, and to date no clear candidate gene has been identified
from this approach as contributing to BMD in the general population. Consequently,
research in osteoporosis genetics has moved to the more powerful and comprehensive
approach of genome-wide association studies to make progress.

Genome-wide association studies and osteoporosis

Several groups worldwide are currently performing genome-wide association studies
in osteoporosis, mostly studying general population cohorts, particularly focusing
on BMD. An early screen from the Framingham study [51] lacked sufficient marker density and statistical power, and no findings of genome-wide
significance were reported. Two recent studies, examining larger cohorts and using
denser marker sets, have been more successful.

deCODE Genetics [52] studied 5,861 men and women from the general population, initially testing more than
300,000 single nucleotide polymorphisms (SNPs), and then following up 74 SNPs in a
further cohort of 7,925 Icelandic, Australian, and Danish individuals. Five regions
were identified that achieved genome-wide significance for association with BMD. In
two cases, these SNPs were in genes that are known to be involved in bone development
or turnover, including RANKL (encoding receptor activator of nuclear factor-κ) and its antagonist OPG (encoding osteoprotegerin). Two novel regions included an area on chromosome 1p36
close to the gene ZBTB40 (encoding zinc finger and ETB domain containing 40) and, somewhat surprisingly, the
major histocompatibility complex. Significant association was also seen near to ESR1 (encoding estrogen receptor-α), a gene previously associated with low BMD. However,
all bar one of the associated markers lie not in ESR1 itself but in an open reading frame gene C6orf97, which is currently of unknown expression and function. This may prove to be the
primary associated gene.

Notable results were also seen for SNPs in a number of other candidate genes previously
studied in osteoporosis, although not achieving genome-wide significance in this study.
These included SNPs in SOST, in the glucocorticoid receptor gene NR3C1 (in the top 500 BMD-associated SNPs overall), and in the vitamin D receptor gene and
LRP5 (in the top 1,000 SNPs). It is therefore likely that other true osteoporosis-associated
SNPs will be identified among these less strongly associated markers.

The study also investigated association with fracture, in a cohort including a total
of 4,406 fracture cases and 36,785 control individuals [52]. No gene achieved genome-wide significance for fracture, but moderate levels of association
(P = 10-3 to 10-4) were seen for the 1p36 region, the major histocompatibility complex, RANK, and two regions not initially detected through association with BMD, namely 2p16
and 11p11 (the latter containing the gene LRP4). When tested in the overall BMD cohort, these regions did achieve moderate level
association with BMD (2p16, P = 8 × 10-7; LRP4, P = 3 × 10-4). Thus, no gene was identified to have significant association with fracture but
not with BMD. This lends support to the approach of studying BMD as the primary phenotype.

One further point to note illustrates the importance of adequately powered studies
of sufficient marker density. ESR1 variants associated with BMD were not associated with fracture; this is in disagreement
with a prospective meta-analysis of 18,917 individuals performed by the GENOMOS consortium
[53], which identified association with fracture but not BMD. The meta-analysis studied
two intronic SNPs in ESR1, neither of which exhibited any association in the deCODE study. The difference in
the findings probably relates to the low coverage of ESR1 genetic variation in the GENOMOS study, which was estimated at only about 30% [54].

The effect size of the fracture associated variants in the deCODE study [52] was small, with risk ratios ranging between 1.06 to 1.15. Individually, they are
not of great use in prediction of fracture risk, which will probably require computation
of risk from combinations of markers. The current capacity of these tests to predict
fracture is illustrated in Figure 1. Using the findings from the discovery component of the study, we calculated the
posterior probability of a fracture for allele carriers of the five SNPs most strongly
associated with fracture (assuming a dominant model, Hardy-Weinberg equilibrium, and
no interaction between markers [that their effects are additive]). This combination
was associated with a likelihood ratio of fracture of 2.25 (the risk of fracture was
increased by 2.25 in carriers of all five SNPs) and a likelihood of fracture of 0.75
in those who did not carry the SNPs. The combination of carriage of all five SNPs
was expected to be present in 50% of fracture cases and 47.5% of control individuals,
and thus is informative for a large proportion of the population. With increasing
numbers of markers available, better predictive performance will be possible, although
larger combinations of markers will be relevant to smaller numbers of people. How
such genetic tests interact with traditional osteoporosis risk factors (such as BMD)
has yet to be established.

Figure 1. Fracture risk given genetic marker findings. Presented is the post-test probability
of fracture given the pre-test risk and findings at five most strongly fracture-associated
SNPs in deCODE osteoporosis genome-wide association study [52]. P(F+/MARKERS+) indicates the probability of fracture in carriers of all five SNP
risk alleles. P(F-/MARKERS-) indicates the probability of no fracture in individuals
negative for all five SNP risk alleles. P(F-/MARKERS+) indicates the probability of
fracture in individuals negative for all five SNP risk alleles. P(F+/MARKERS-) indicates
the probability of fracture in carriers of all five SNP risk alleles. SNP, single
nucleotide polymorphism.

In the other genome-wide association study recently published [47], 2,094 twins from the TwinsUK cohort were examined, and then a two-phase replication
study performed in further BMD cohorts (n = 4,877 total) and for association with
fracture (n = 660 fractures, n = 6,639 nonfracture controls). Two genes reached genome-wide
significance, namely LRP5 and OPG. Marginal fracture association was also observed (P = 0.006) in carriers of risk alleles at both genes, but the effect size of this association
was large (odds ratio = 1.33) and combination common (22%), suggesting that it may
be a useful prognostic test if the fracture association can be confirmed.

These studies illustrate the massive sample sizes required to identify osteoporosis
genes, particularly if fracture is used as the study end-point. Studies of younger
fracture cohorts are likely to be more fruitful, given the greater heritability suggested
for hip fracture in younger cases, but these will be harder to recruit, because most
fractures occur in older age cohorts. The small effect size seen with the fracture-associated
variants indicates that future studies will need to be adequately powered to detect
variants with odds ratios lower than those observed here, and will thus need to be
extremely large. For example, assuming an equal number of cases and controls, an SNP
with minor allele frequency of 0.25 in linkage disequilibrium with a fracture-associated
SNP with D' = 0.8, and a statistical threshold for significance of P = 10-7, for a marker with an additive relative risk of 1.1 more than 17,000 fracture cases
will be required. The genetic assumptions underlying this calculation are actually
optimistic. Much larger numbers will be required if the associated variants are less
common, if the mapping SNP and fracture-associated SNP have different allele frequencies,
or if gene-gene interactions are involved. In reality, the numbers required will be
beyond the reach of individual studies, and large consortia and meta-analytical methods
will be required to achieve adequate power. The genetics community is well aware of
this, and the recently established European Union funded 'Genetic Factors in Osteoporosis'
(GEFOS) consortium has rapidly established itself as the central organization for
such efforts worldwide.

Future directions

There is little doubt that genome-wide association studies will identify more genes
that are involved in bone fragility than those that have been reported so far. Genome-wide
association analysis in unselected populations has proven to be a powerful method
with which to identify common genes of moderate effect size. The studies performed
to date are not sufficiently powered to identify genes of smaller population effect,
and may not identify some forms of human genetic variation that are likely to influence
bone fragility. No single approach is likely to identify all bone fragility genes,
and a variety of different methods are either being developed or are in use to tackle
the problem.

The usual mantra for complex diseases is that larger studies are needed and are likely
to make many further contributions to what we know. Size isn't everything, however;
it is equally likely that more efficient study designs of selected cohorts aimed at
maximizing the power to detect association will make further significant discoveries,
and at considerably lower genotyping cost than simply increasing the sample size.
In particular, cohorts recruited to minimize genetic heterogeneity are likely to be
valuable. The genetic control over skeletal development is known to vary between sites
and sexes, and it is likely that genes make different contributions at different ages.
Thus, cohorts recruited to investigate osteoporosis genetics focusing on a particular
site, age, and sex are likely to have greater power to identify genes than studies
of cohorts recruited unselected from the general population. Our group recently demonstrated
this with a proof-of-principle study [45], which easily confirmed the known association of LRP5 with BMD in a cohort of just 320 postmenopausal women selected for extreme BMD at
the hip.

Meta-analysis may also produce findings that individual screens have missed. Although
in the past competition between groups hindered data sharing for meta-analysis, there
is a solid recognition among osteoporosis researchers that collaboration and open
data sharing will be essential both for gene discovery and for replication.

Genetics research is technology driven. Genome-wide association analysis was made
possible by chip-based SNP genotyping technology. A further genetics revolution is
being brought about by the development of next-generation sequencers capable of producing
up to 20 gigabases per run, which has reignited interest in monogenic diseases. It
is likely that in a high proportion of individuals with extreme phenotypes (such as
extreme high or low BMD in humans) a monogenic – usually rare – mutation underlies
their extreme phenotype, as has been demonstrated, for example, with osteogenesis
imperfecta type 1 and Marfan's syndrome. When there were not enough family members
to help localize the gene by traditional linkage methods, or the individual did not
fit a known syndrome that would allow population studies to be conducted, the mutations
in these cases could not be identified. With the new sequencing capacity it will be
possible to sequence extremely large proportions of the genome (such as, for example,
all exons) in a single sequencing run. Such cases may be studied again, and it is
highly likely that new disease genes affecting bone fragility will be identified,
not just of relevance to these extreme phenotypes but also to control of BMD in the
general population.

Two further influences on human variation that have yet to be addressed significantly
in osteoporosis include copy number variation (CNV) and gene-gene interaction. CNV
is known to be common throughout the genome and is likely to influence gene expression.
High-throughput, accurate genotyping methods for CNV are still in development, but
array-based methods show promise. Gene-gene interaction is known from mouse models
to influence skeletal development significantly [55] and is thus likely also to contribute to human skeletal development. All genome-wide
association studies to date have been single-marker studies, but it is likely that
once sufficient cases have been screened, more complex genetic models will be tested.

Mouse genetics to date has contributed much to what we know about the genetic epidemiology
of bone fragility and associated phenotypes, such as BMD and bone microarchitecture.
Hypothesis-free gene mapping of bone fragility genes has made slow progress though.
Congenic approaches, investigating the genetic causes of differences in bone parameters
between inbred mouse strains, has yielded some success in the identification of Alox12, implicating the lipoxygenase system in osteoporosis [56]. However, the inbred nature of the mice restricts the mapping resolution that can
be obtained, and most established linkages with bone parameters have not resulted
in identification of the causative gene. An alternate approach is ENU mutagenesis,
in which male mice are treated with the alkylating agent ethinyl-nitrosourea, causing
point mutations in sperm DNA. Offspring of these mice carry these mutations. By screening
thousands of offspring of mutagenized mice, mice with phenotypes generally caused
by monogenic point mutations caused by the ENU can be identified. These monogenic
variants are much easier to map than congenic genes, and because the mutations concerned
are not as severe as knock-out or knock-in methods, the models are more physiological.
This approach is being used by a number of groups worldwide to create new mouse models
of osteoporosis.

Conclusion

This is a time of great excitement in the world of genetics generally, and in osteoporosis
genetics specifically. The publication of the Wellcome Trust Case Control Consortium
(less than 12 months ago at the time of writing) [57] was not only an enormous leap forward in identifying that genes that underlie complex
genetic disorders such as inflammatory bowel disease, ankylosing spondylitis, and
type 1 diabetes. It also provided proof that the approach adopted was going to work
for other complex quantitative traits such as osteoporosis. The success of early genome-wide
association studies in osteoporosis supports this position. Already, these studies
have identified novel pathways that contribute to control of BMD and bone fragility
with possible therapeutic targets. The possibility of genetic prognostic tests, adding
to existing predictive information from BMD, is likely to become a reality within
the next decade. Hopefully, the frustrations of the past few decades have taught the
genetics community that careful phenotyping, sophisticated study design, adequately
powered cohorts, and collaboration are key elements to successful gene identification.
We have finished with the beginning and can now see ways and means to achieve a successful
future.